1. Technical Field
The invention relates generally to the extraction and/or presentation of glucose concentrations estimated as a function of time into a format that facilitates conveyance of the underlying information. More particulauly, glucose concentration histories are presented in terms of risk of behavior, in a video format, and/or in an audio format.
2. Description of the Prior Arts
Diabetes
Diabetes is a chronic disease that results in abnormal production and use of insulin, a hormone that facilitates glucose uptake into cells. While a precise cause of diabetes is unknown, genetic factors, environmental factors, and obesity play roles. Diabetics have increased risk in three broad categories: cardiovascular heart disease, retinopathy, and neuropathy. Diabetics often have one or more of the following complications: heart disease and stroke, high blood pressure, kidney disease, neuropathy, e.g. nerve disease and amputations, retinopathy, diabetic ketoacidosis, skin conditions, gum disease, impotence, and fetal complications. Diabetes is a leading cause of death and disability worldwide. Moreover, diabetes is merely one among a group of disorders of glucose metabolism that also includes impaired glucose tolerance and hyperinsulinemia, which is also known as hypoglycemia.
Diabetes Prevalence and Trends
The prevalence of individuals with diabetes is increasing with time. The World Health Organization (WHO) estimates that diabetes currently afflicts 154 million people worldwide. There are 54 million people with diabetes living in developed countries. The WHO estimates that the number of people with diabetes will grow to 300 million by the year 2025. In the United States, 15.7 million people or 5.9 percent of the population are estimated to have diabetes. Within the United States, the prevalence of adults diagnosed with diabetes increased by 6% in 1999 and rose by 33% between 1990 and 1998. This corresponds to approximately eight hundred thousand new cases every year in America. The estimated total cost to the United States economy alone exceeds $90 billion per year. Diabetes Statistics, National Institutes of Health, Publication No. 98-3926, Bethesda, Md. (November 1997).
Long-term clinical studies demonstrate that the onset of diabetes related complications is significantly reduced through proper control of blood glucose concentrations. The Diabetes Control and Complications Trial Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, N. Eng. J. of Med., 329:977-86 (1993); U.K. Prospective Diabetes Study (UKPDS) Group, Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes, Lancet, 352:837-853 (1998); and Y. Ohkubo, H. Kishikawa, E. Araki, T. Miyata, S. Isami, S. Motoyoshi, Y. Kojima, N. Furuyoshi, M. Shichizi, Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study, Diabetes Res. Clin. Pract., 28:13-117 (1995).
A vital element of diabetes management is the self-monitoring of blood glucose concentration by diabetics in the home environment. However, current monitoring techniques discourage regular use due to the inconvenient and painful nature of drawing blood or interstitial fluid through the skin prior to analysis, The Diabetes Control and Complication Trial Research Group, supra. Unfortunately, recent reports indicate that even periodic measurement of glucose by individuals with diabetes e.g. seven times per day is insufficient to detect important glucose fluctuations and properly manage the disease. In addition, nocturnal monitoring of glucose concentrations is of significant value but is difficult to perform due to the state of existing technology. Therefore, a device, such as a noninvasive glucose analyzer, that provides noninvasive, automatic, and nearly continuous estimations of glucose concentration is identified as a beneficial development for the management of diabetes. Implantable glucose concentration analyzers coupled to an insulin delivery system providing an artificial pancreas are also being pursued.
Timing
Historically, the extreme majority of traditional glucose concentration determinations have been discrete glucose concentration measurements at irregular or widely spaced time intervals. For example, a subject or user may test their fasting glucose concentration in the morning. Similarly, the user optionally generates a glucose concentration determination before or after a meal or according to a fixed protocol. A number of traditional technologies have been used for these determinations, such as enzymatic, electro-enzymatic, or colorimetric based technologies.
A discrete blood or body chemistry analyte determination is appropriate for the determination of body constituents that do not change rapidly with time. For example, the measurement of plasma albumin does not change rapidly. Other body constituents, such as triglycerides, change more rapidly and hence information is generated based upon their concentration change with time. However, even for these analytes fasting concentrations and/or widely separated concentration determinations are often adequate.
Widely separated in time discrete glucose concentration determinations are less appropriate than a history of well timed glucose concentration determinations. A major problem with widely separated or discrete glucose determinations is that the glucose concentration in the body may change rapidly, especially in diabetics or in pre-diabetics. The degree and/or rate of glucose concentration change is dependent upon a large number of factors including but not limited to the subject's insulin resistance, exercise level, mitigating drug intake, and carbohydrate and food intake history. The transient hyperglycemic and hypoglycemic periods, that are often missed with irregular testing, are of great interest to both the medical professional and tested subject. Recently, a number of analyzers that facilitate a larger number of glucose concentration estimations during a time period have become available that generate semi-continuous or continuous glucose concentrations for a subject. These technologies are an attempt at catching the hyperglycemic and hypoglycemic periods and are reviewed, infra.
Invasive and Minimally Invasive Technologies
A number of continuous and semi-continuous analyzers have recently become available. These glucose concentration analyzers are invasive or minimally invasive in nature.
A first example is an alternative invasive electrochemical-enzymatic sensor. One instance is a Cygnus (Redwood City, Calif.) GLUCOWATCH, which is semi-continuous and minimally invasive. The GLUCOWATCH provides only one reading every twenty minutes, each delayed by at least 10 minutes due to the measurement process. The measurement is made through an alternative invasive electrochemical-enzymatic sensor on a sample of interstitial fluid which is drawn through the skin using iontophoresis. Consequently, the limitations of the device include the potential for significant skin irritation, collection of a biohazard, and a limit of three readings per hour. The GLUCOWATCH presents up to a three day history of glucose concentrations as a function of time.
A second example is a partially implantable glucose concentration analyzer. One instance is the MINIMED (Northridge, Calif.) continuous glucose monitoring system, which is a short-term implantable glucose analyzer. The MINIMED system is capable of providing a glucose concentration profile for up to seventy-two hours. The system records a glucose value every five minutes. The technology behind the MINIMED system relies on a probe being invasively implanted into a subcutaneous region followed by a glucose oxidase based reaction producing hydrogen peroxide, which is oxidized at a platinum electrode to produce an analytical current. Notably, the MINIMED system automatically shifts glucose determinations by ten minutes in order to accommodate for a potential dynamic lag between the blood and interstitial glucose. See T. Gross, B. Bode, D. Einhorn, D. Kayne, J. Reed, N. White, and J. Mastrototaro, Performance evaluation of the MiniMed continuous glucose monitoring system during patient home use, Diabetes Technology & Therapeutics, vol. 2, pp. 49-56 (2000). Inherent in these approaches are health risks due to the sensor implantation, infections, patient inconvenience, and measurement delay.
A third example is a continuous monitoring fluorescence based glucose sensor. A. Colvin, Optical-based sensing devices especially for in-situ sensing in humans, U.S. Pat. No. 6,304,766 (Oct. 16, 2001); A. Colvin, G. Daniloff, A. Kalivretenos, D. Parker, E. Ullman, A. Nikolaitchik, Detection of analytes by fluorescent lanthanide metal chelate complexes containing substituted ligands, U.S. Pat. No. 6,334,360 (Feb. 5, 2002); and J. Lesho, Implanted sensor processing system and method for processing implanted sensor output, U.S. Pat. No. 6,400,974 (Jun. 4, 2002) describe an indicator molecule that is combined into an implantable device that is coupled via telemetry to an external device. The device works via an indicator molecule that reversibly binds to glucose. With a light emitting diode for excitation, the indicator molecule fluoresces in the presence of glucose. This device is an example of a short-term implantable with development towards a long-term implantable.
Notably, none of these technologies are noninvasive. Further, none of these technologies offer continuous glucose concentration determination. Finally, none of these technologies use data reduction to represent the glucose concentrations as presented herein.
Noninvasive Technologies
There exist a number of noninvasive approaches for estimation or determination of glucose concentration. These approaches vary widely, but have at least two common steps. First, an apparatus is used to acquire a reading from the body without obtaining a biological sample. Second, an algorithm is used to convert this reading into an estimated glucose concentration.
Noninvasive techniques sample skin, the skin surface, interstitial fluid, tissue, and/or blood. Regions or volumes of the body subjected to noninvasive measurements are: a hand, finger, palmar region, base of thumb, forearm, between the wrist and elbow on the back of the arm, volar aspect of the forearm, dorsal aspect of the forearm, upper arm, head, earlobe, eye, tongue, chest, torso, abdominal region, thigh, calf, foot, plantar region, and toe. It is important to note that noninvasive techniques do not have to be based upon spectroscopy. For example, a bioimpedence meter is a noninvasive device. In this document, any device that reads glucose concentration from the body without penetrating the skin and collecting a biological sample is referred to as a noninvasive glucose concentration analyzer.
One species of noninvasive glucose concentration analyzers are those analyzers based upon the collection and analysis of spectra. Typically, a noninvasive apparatus uses some form of spectroscopy to acquire the signal or spectrum from the body. Spectroscopic techniques used for noninvasive glucose concentration estimation include but are not limited to Raman and fluorescence, as well as techniques using light from the ultraviolet through the infrared [ultraviolet (200 to 400 nm), visible (400 to 700 nm), near-infrared (700 to 2500 nm or 14,286 to 4000 cm−1), and infrared (2500 to 14,285 nm or 4000 to 700 cm−1)]. These techniques are used with a range of preprocessing approaches, outlier detection, chemometrics, calibration, and prediction techniques for noninvasive, minimally invasive, and implantable devices.
There are a number of reports on noninvasive glucose technologies. Some of these relate to general instrumentation configurations required for noninvasive glucose concentration determination while others refer to sampling technologies. Those related to the present invention are briefly reviewed here:
General Instrumentation
K. Schlager, Non-invasive near infrared measurement of blood analyte concentrations, U.S. Pat. No. 4,882,492, (Nov. 21, 1989) describes a dual beam noninvasive glucose analyzer.
R. Barnes, J. Brasch, D. Purdy, W. Lougheed, Non-invasive determination of analyte concentration in body of mammals, U.S. Pat. No. 5,379,764 (Jan. 10, 1995) describe a noninvasive glucose concentration determination analyzer that uses data pretreatment in conjunction with a multivariate analysis to determine blood glucose concentrations.
P. Rolfe, Investigating substances in a patient's bloodstream, U.K. patent application ser. no. 2,033,575 (Aug. 24, 1979) describes an apparatus for directing light into the body, detecting attenuated backscattered light, and utilizing the collected signal to determine glucose concentrations in or near the bloodstream.
C. Dahne, D. Gross, Spectrophotometric method and apparatus for the non-invasive, U.S. Pat. No. 4,655,225 (Apr. 7, 1987) describe a method and apparatus for directing light into a patient's body, collecting transmitted or backscattered light, and determining glucose concentrations from selected near-infrared wavelength bands. Wavelengths include 1560 to 1590, 1750 to 1780, 2085 to 2115, and 2255 to 2285 nm with at least one additional reference signal from 1000 to 2700 nm.
A particular range for noninvasive glucose concentration estimation in diffuse reflectance mode is about 1100 to 2500 nm or ranges therein, see K. Hazen, Glucose determination in biological matrices using near-infrared spectroscopy, doctoral dissertation, University of Iowa, (1995).
M. Robinson, K. Ward, R. Eaton, D. Haaland, Method and apparatus for determining the similarity of a biological analyte from a model constructed from known biological fluids, U.S. Pat. No. 4,975,581 (Dec. 4, 1990) describe a method and apparatus for measuring a concentration of a biological analyte such as glucose using infrared spectroscopy in conjunction with a multivariate model. The multivariate model is constructed from a plurality of known biological fluid samples.
J. Hall, T. Cadell, Method and device for measuring concentration levels of blood constituents non-invasively, U.S. Pat. No. 5,361,758 (Nov. 8, 1994) describe a noninvasive device and method for determining analyte concentrations within a living subject, using polychromatic light, a wavelength separation device, and an array detector. The apparatus uses a receptor shaped to accept a fingertip with means for blocking extraneous light.
S. Malin, G Khalil, Method and apparatus for multi-spectral analysis of organic blood analytes in noninvasive infrared spectroscopy, U.S. Pat. No. 6,040,578 (Mar. 21, 2000) describe a method and apparatus for determination of an organic blood analyte using multi-spectral analysis in the near-infrared. A plurality of distinct nonoverlapping regions of wavelengths are incident upon a sample surface, diffusely reflected radiation is collected, and the analyte concentration is determined via chemometric techniques.
J. Garside, S. Monfre, B. Elliott, T. Ruchti, G. Kees, Fiber optic illumination and detection patterns, shapes, and locations for use in spectroscopic analysis, U.S. Pat. No. 6,411,373, (Jun. 25, 2002) describe the use of fiber optics for use as excitation and/or collection optics with various spatial distributions.
Specular Reflectance
R. Messerschmidt, D. Sting Blocker device for eliminating specular reflectance from a diffuse reflectance spectrum, U.S. Pat. No. 4,661,706 (Apr. 28, 1987) describe a reduction of specular reflectance by a mechanical device. A blade-like device skims the specular light before it impinges on the detector. A disadvantage of this system is that it does not efficiently collect diffusely reflected light and the alignment is problematic.
R. Messerschmidt, M. Robinson, Diffuse reflectance monitoring apparatus, U.S. Pat. No. 5,636,633 (Jun. 10, 1997) describe a specular control device for diffuse reflectance spectroscopy using a group of reflecting and open sections.
R. Messerschmidt, M. Robinson, Diffuse reflectance monitoring apparatus, U.S. Pat. No. 5,935,062 (Aug. 10, 1999) and R. Messerschmidt, M. Robinson, Diffuse reflectance monitoring apparatus, U.S. Pat. No. 6,230,034 (May 8, 2001) describe a diffuse reflectance control device that discriminates between diffusely reflected light that is reflected from selected depths. This control device additionally acts as a blocker to prevent specularly reflected light from reaching the detector.
Malin, supra describes the use of specularly reflected light in regions of high water absorbance such as 1450 and 1900 nm to mark the presence of outlier spectra wherein the specularly reflected light is not sufficiently reduced.
K. Hazen, G. Acosta, A. Abul-Haj, R. Abul-Haj, Apparatus and method for reproducibly modifying localized absorption and scattering coefficients at a tissue measurement site during optical sampling, U.S. Pat. No. 6,534,012 (Mar. 18, 2003) describe a mechanical device for applying sufficient and reproducible contact of the apparatus to the sampling medium to minimize specular reflectance. Further, the apparatus allows for reproducible applied pressure to the sampling site and reproducible temperature at the sampling site.
Sample Preparation
B. Wenzel, S. Monfre, T. Ruchti, K. Meissner, F. Grochocki, T. Blank, J. Rennert, A method for quantification of stratum corneum hydration using diffuse reflectance spectroscopy, U.S. Pat. No. 6,442,408, (Aug. 27, 2002) describe a method and apparatus for determination of tissue variability, such as water content of the epidermal ridge and penetration depth of incident light.
Temperature
K. Hazen, Glucose Determination in Biological Matrices Using Near-Infrared Spectroscopy, doctoral dissertation, University of Iowa (1995) describes the adverse effect of temperature on near-infrared based glucose concentration determinations. Physiological constituents have near-infrared absorbance spectra that are sensitive, in terms of magnitude and location, to localized temperature and the sensitivity impacts noninvasive glucose concentration determination.
Coupling Fluid
A number of sources describe coupling fluids with important sampling parameters.
Index of refraction matching between the sampling apparatus and sampled medium is well known. Glycerol is a common index matching fluid for optics to skin.
R. Messerschmidt, Method for non-invasive blood analyte measurement with improved optical interface, U.S. Pat. No. 5,655,530 (Aug. 12, 1997), and R. Messerschmidt, Method for non-invasive blood analyte measurement with improved optical interface, U.S. Pat. No. 5,823,951 describe an index-matching medium for use between a sensor probe and the skin surface. The index-matching medium is a composition containing perfluorocarbons and chlorofluorocarbons.
M. Robinson, R. Messerschmidt, Method for non-invasive blood analyte measurement with improved optical interface, U.S. Pat. No. 6,152,876 (Nov. 28, 2000) and M. Rohrscheib, C. Gardner, M. Robinson, Method and apparatus for non-invasive blood analyte measurement with fluid compartment equilibration, U.S. Pat. No. 6,240,36 (May 29, 2001) describe an index-matching medium to improve the interface between the sensor probe and skin surface during spectroscopic analysis. The index-matching medium is preferably a composition containing chlorofluorocarbons with optional perfluorocarbons.
T. Blank, G. Acosta, M. Mattu, S. Monfre, Fiber optic probe guide placement guide, U.S. Pat. No. 6,415,167 (Jul. 2, 2002) describe a coupling fluid of one or more perfluoro compounds where a quantity of the coupling fluid is placed at an interface of the optical probe and measurement site. Perfluoro compounds do not have the toxicity associated with chlorofluorocarbons.
Positioning
T. Blank, supra describes the use of a guide in conjunction with a noninvasive glucose concentration analyzer in order to increase precision of the location of the sampled tissue site resulting in increased accuracy and precision in noninvasive glucose concentration estimations.
J. Griffith, P. Cooper, T. Barker, Method and apparatus for non-invasive blood glucose sensing, U.S. Pat. No. 6,088,605 (Jul. 11, 2000) describe an analyzer with a patient forearm interface in which the forearm of the patient is moved in an incremental manner along the longitudinal axis of the patient's forearm. Spectra collected at incremental distances are averaged to take into account variations in the biological components of the skin. Between measurements rollers are used to raise the arm, move the arm relative to the apparatus and lower the arm by disengaging a solenoid causing the skin lifting mechanism to lower the arm into a new contact with the sensor head.
Pressure
E. Chan, B. Sorg, D. Protsenko, M. O'Neil, M. Motamedi, A. Welch, Effects of compression on soft tissue optical properties, IEEE Journal of Selected Topics in Quantum Electronics, Vol. 2, no. 4, pp. 943-950 (1996) describe the effect of pressure on absorption and reduced scattering coefficients from 400 to 1800 nm. Most specimens show an increase in the scattering coefficient with compression.
K. Hazen, G. Acosta, A. Abul-Haj, R. Abul-Haj, Apparatus and method for reproducibly modifying localized absorption and scattering coefficients at a tissue measurement site during optical sampling, U.S. Pat. No. 6,534,012 (Mar. 18, 2003) describe in a first embodiment a noninvasive glucose concentration estimation apparatus for either varying the pressure applied to a sample site or maintaining a constant pressure on a sample site in a controlled and reproducible manner by moving a sample probe along the z-axis perpendicular to the sample site surface. In an additional described embodiment, the arm sample site platform is moved along the z-axis that is perpendicular to the plane defined by the sample surface by raising or lowering the sample holder platform relative to the analyzer probe tip. The '012 patent further teaches proper contact to be the moment specularly reflected light is about zero at the water bands at 1950 and 2500 nm.
M. Makarewicz, M. Mattu, T. Blank, G. Acosta, E. Handy, W. Hay, T. Stippick, B. Richie, Method and apparatus for minimizing spectral interference due to within and between sample variations during in-situ spectral sampling of tissue, U.S. patent application Ser. No. 09/954,856 (filed Sep. 17, 2001) describe a temperature and pressure controlled sample interface. The means of pressure control are a set of supports for the sample that control the natural position of the sample probe relative to the sample.
Data Processing
R. Barnes, J. Brasch, Non-invasive determination of glucose concentration in body of patients, U.S. Pat. No. 5,070,874, (Dec. 10, 1991) describe a method of collecting near-infrared noninvasive spectra, preprocessing with an nth derivative, and determining a glucose concentration from the resulting spectrum.
Several approaches exist that employ diverse preprocessing methods to remove spectral variation related to the sample and instrumental variation including normalization, smoothing, derivatives, multiplicative signal correction, [P. Geladi, D. McDougall, H. Martens, Linearization and scatter-correction for near-infrared reflectance spectra of meat, Applied Spectroscopy, vol. 39, 491-500, (1985)], standard normal variate transformation, [R. Barnes, M. Dhanoa, S. Lister, Applied Spectroscopy, 43, 772-777, (1989)], piecewise multiplicative scatter correction, [T. Isaksson and B. Kowalski, Applied Spectroscopy, 47, 702-709, (1993)], extended multiplicative signal correction, [H. Martens, E. Stark, J. Pharm Biomed Anal, 9, 625-635, (1991)], pathlength correction with chemical modeling and optimized scaling, [GlucoWatch automatic glucose biographer and autosensors, Cygnus Inc., Document #1992-00, Rev. March (2001)], and finite impulse response filtering, [S. Sum, Spectral signal correction for multivariate calibration, Doctoral Dissertation, University of Delaware, (1998); S. Sum, and S. Brown, Standardization of fiber-optic probes for near-infrared multivariate Calibrations, Applied Spectroscopy, Vol. 52, No. 6, 869-877, (1998); and T. Blank, S. Sum, S. Brown, S. Monfre, Transfer of near-infrared multivariate calibrations without standards, Analytical Chemistry, 68, 2987-2995, (1996)].
In addition, a diversity of signal, data or pre-processing techniques are commonly reported with the fundamental goal of enhancing accessibility of the net analyte signal [D. Massart, B. Vandeginste, S. Deming, Y. Michotte, L. Kaufman, Chemometrics: a textbook, New York, Elsevier Science Publishing Company, Inc., 215-252, (1990); A. Oppenheim, R. Schafer, Digital Signal Processing, Englewood Cliffs, N.J.: Prentice Hall, 1975, 195-271; M. Otto, Chemometrics, Weinheim: Wiley-VCH, 51-78, (1999); K. Beebe, R. Pell, M. Seasholtz, Chemometrics A Practical Guide, New York: John Wiley & Sons, Inc., 26-55, (1998); M. Sharaf, D. Illman and B. Kowalski, Chemometrics, New York: John Wiley & Sons, Inc., 86-112, (1996); and A. Savitzky, M. Golay, Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., vol. 36, no. 8, 1627-1639, (1964)]. A goal of these techniques is to attenuate the noise and instrument variation while maximizing the signal of interest.
Existing Glucose Data Management
Typically, an invasive, minimally invasive, noninvasive, or implantable glucose concentration analyzer presents discrete glucose concentrations. For example, a glucose concentration determination is performed before breakfast, before lunch, and before supper resulting in three glucose concentrations. Some users act upon each discrete glucose concentration without the benefit of seeing long or medium term data. Some users use meters that automatically record a limited history of the glucose concentrations into memory or they manually record some or all of the numbers into a notebook. The user or medical care specialist looks at these numbers and tries to adjust diagnosis or treatment with the data. A meter that records glucose information may record data fields that consist of date, time, and glucose concentration. Other meters may record user identification in addition to the above fields.
Representative glucose concentration data with associated time and user elements are presented in Table 1. A problem with this typical presentation of data in Table format is that underlying information is not readily apparent. For example, the data in Table 1 represents glucose concentrations collected in less than one week. A doctor often has only a minute to exam the data and to decide on an adjustment to the patient's diabetes management protocol. Deciding a treatment based upon the data as presented in Table 1 is difficult. A medical professional may only have time to see highs or lows in the glucose concentration. This problem becomes considerably more complex as the number of glucose concentrations determined in a day increases and/or as the total number of days recorded increases.
TABLE 1Typical Glucose Concentration DataGlucoseSubjectConcentrationi.d.DayTimea.m./p.m.(mg/dL)11 7:27a.m.1201111:33a.m.1882112:16p.m.26711 6:27p.m.19612 7:33a.m.11512 8:15a.m.1171211:32a.m.21512 6:30p.m.23222 7:19p.m.17813 7:25a.m.1261311:50a.m.19633 3:12p.m.9213 7:14p.m.16813 8:14a.m.1331412:02p.m.17524 2:15p.m.31214 6:46p.m.21914 7:18a.m.1151511:54a.m.18935 4:49p.m.8815 6:46p.m.24816 7:12a.m.97
Some devices present the glucose concentration determined as a function of time. Many devices, such as a fingerstick meter, do not commonly distinguish between users. Hence, the tabulated glucose concentrations as a function of time may represent many users of the device. Even a more advanced device that keeps track of user identification or of a tracking number may have difficulties in presenting the underlying information in the data. For example, the glucose concentrations for only subject number 1 of the Table 1 data is presented in FIG. 1. Again, the underlying patterns of glucose control, or lack of control, are not readily apparent as presented.
A large number of minimally invasive, noninvasive glucose, semi-continuous, and continuous analyzers have recently become available or have been presented in the literature. Many of these analyzers are capable of generating large numbers of glucose concentration readings. Currently, methods of reducing large amounts of glucose data into more manageable information are not used in conjunction with noninvasive glucose meters, semi-continuous, or continuous glucose analyzers. Clearly, as technologies allow for more frequent glucose concentration determinations data management techniques that allow the extraction and presentation of diabetes information from the underlying data becomes increasingly important.
While a number of instruments and data processing approaches exist as described, supra, none of these reports describe methods of reducing the voluminous data into information that is readily absorbed by a professional or lay user in a short period of time.